1. Field of the Invention
The present invention relates to information-processing apparatuses and the like for providing, for example, recommendation information using information such as the purchase history of products purchased by two or more users.
2. Description of Related Art
Conventionally, there is an information-processing system for simultaneously realizing both a content recommendation with more pertinence based on the name and the value of an item that a user is strongly interested in and a content recommendation in consideration of the sequentially of content utilization (see JP 2005-293384A (p. 1, FIG. 1, etc.)). This system has a content usage history information storage and management portion in which the content usage history information of a user is stored and managed, a content usage shift information-computing portion that computes content usage shift information based on the content usage history information, a content usage shift information storage and management portion in which the content usage shift information is stored and managed, a content metadata information storage and management portion in which content metadata information is stored and managed, and a content recommendation information-generating portion that generates content recommendation information based on the content usage history information, the content usage shift information, and the content metadata information.
Furthermore, there is a system for extracting the characteristics of each item name for an individual, and recommending content based on the characteristics of each item name for the individual (see JP 2004-362011A (p. 1, FIG. 1, etc.)). In this system, the user's item-categorized preference information of a targeted user is acquired with respect to preset item names. Reference is made to the acquired user's item-categorized preference information, and if an item name appears a number of times equal to or larger than a threshold value preset for the item name, then its item value is extracted. Accordingly, content information containing this item value as the value of a target item name is acquired, and the acquired content information is recommended to the user.
Furthermore, there is a system for improving the possibility of realizing product purchase and for providing a comprehensive recommendation service (see JP 2002-117292A (p. 1, FIG. 1, etc.)). In this system, if a user accesses a server of a music distribution shop A via a network connection service using a mobile phone and purchases music software, then the server of the shop A transmits the purchase information to a center, and the center searches for concert information for the singer following its recommendation rules, and transmits the recommendation to the mobile phone via a network connection service. Also, in this system, if the user purchases a concert ticket using the mobile phone from a server of a ticket shop B, then the server of the shop B transmits the purchase information to the center, and the center searches for the reservation status of an airplane and the like on the concert day in this purchase information, following its recommendation rules, and transmits the recommendation to the mobile phone.
Furthermore, there is the technique of a recommendation engine named GroupLens that automatically ranks netnews (Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: An Open Architecture for Collaborative Filtering of Netnews. Proceedings of the 1994 Computer Supported Collaborative Work Conference, pp. 175-186 (1994)). Moreover, a lot of research has been conducted on recommendation based on collaborative filtering (Balabanovic, M. and Shoham, Y.: Fab: Content-Based, Collaborative Recommendation. Communications of the ACM, Vol. 40, Issue 3, pp. 66-72 (1997), Herlocker, J. L., Konstan, J. A., Borchers, A. and Riedl, J.: An Algorithmic Framework for Performing Collaborative Filtering, Proceedings of the 22nd annual international ACM SIGIR, pp. 230-237 (1999), Sarwar, B., Karypis, G., Konstan, J. and Riedl, J.: Item-Based Collaborative Filtering Recommendation Algorithms, Proceedings of the 10th International Conference on World Wide Web, pp. 285-295 (2001), and Linden, G., Smith, B., and York, J.: Amazon.com Recommendations: Item-to-Item Collaborative Filtering, Internet Computing, IEEE, Volume 7, pp. 76-80 (2003)).
However, in conventional systems, objects have not been grouped using a history of operations (e.g., purchases, browsing of information, etc.) performed by users on one or more objects (e.g., products or services).
Furthermore, objects have not been grouped using input information, which is information on objects input by users.
Moreover, in the case where an object that users are interested in is dynamically changed, for example, a process of dynamically acquiring a group to which the object belongs and recommending another object in the group has not been performed.
Thus, the precision in recommending products and the like has been low.